Feature Extraction and Classification of Mammographic Masses

1. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Feature Extraction and Classification of Mammographic Masses Presented by, Jignesh Panchal Anuradha Agatheeswaran

2. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Introduction • Breast cancer is a leading cause in women deaths. • Computer-Aided Systems are efficient tools in early detection • of cancer. • Generally the tumors are of two types: • Benign : Round • Malignant : Spiculated. • A computer-aided classification system has been developed • which classifies the mammographic tumors in two classes: • benign or malignant.

3. ECE 8990: Automated Target Recognition Classification of Mammographic Masses System Overview Segmentation Feature Extraction Feature Optimization Performance Evaluation Classification Classified Data

4. ECE 8990: Automated Target Recognition Classification of Mammographic Masses System Overview (Contd.) • Segmentation: Images are manually segmented by the expert • radiologists and the boundaries marked by them are assumed to • be correct. • Feature Extraction: In this study, total 9 features are extracted. • 5 Texture features • 3 Shape features • 1 Age feature • Features are further optimized by using Stepwise Linear • Discriminant Analysis. • Maximum Likelihood Classifier is used for the classification and • the performance is evaluated using leave-one-out testing method.

5. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Mammographic Dataset • Mammographic database for this system is obtained from the • ‘Digital Database for Screening Mammography’, University of • South Florida, Tampa. • In this study, total 73 mammograms are used • 41 Benign • 32 Malignant • The images are compressed to 8 bits/pixel using the software • “heathusf v1.1.0”, provided by USF. • Region of interest is cropped to a size of 1024 x 1024 pixels, • rather than using the entire mammograms.

6. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Mammographic Dataset (Contd.) (1024 x 1024)

7. Mammogram Template (1024 x 1024) (1024 x 1024) ECE 8990: Automated Target Recognition Classification of Mammographic Masses Feature Extraction: Shape Features • Radial Distance Measure (RDM) is a very useful term in the shape • analysis. • RDM: It is basically the Euclidean distance calculated from the • center of the tumor to the boundary pixels and normalized by • dividing with the maximum length.

8. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Shape Features (Contd.) Benign

9. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Shape Features (Contd.) Malignant

10. N 1 ∑ d(i) davg = N I = 1 N 1 ∑ (d(i) - davg )2 σ2 = N I = 1 ECE 8990: Automated Target Recognition Classification of Mammographic Masses Shape Features (Contd.) • Features Extracted: • Mean: • Variance: • Zero crossings

11. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Texture Analysis • Texture features contains the information about the tonal • variations in the spatial domain. • Gray-tone spatial-dependence matrices 0° 45° 135° 90° Direction considered

12. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Texture Analysis (Cont.) • Calculation of all four distance 1 gray-tone spatial-dependence • (GTSD) matrices 0 1 2 3 0 1 2 3 4 X 4 image with 4 gray tone values General form of GTSD matrix 0° 90° 45° 135°

13. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Texture Analysis (Cont.) • Texture features extracted from different directions are • For better accuracy, each texture feature in all direction are summed. • Therefore there are 5 texture features instead of 20.

14. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Feature optimization and Classification • To optimize the feature , stepwise LDA is used. Forward Selection Backward Rejection Features Optimum features Performance measure (PM) of N features Loop M times to get the “most” optimum set of features so as to improve the PM compared to the forward selection Sort according to PM values “Most” optimum features Loop N times to get the optimum set of feature so that the performance measure improves. Optimum features

15. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Feature optimization and Classification (Cont.) • Maximum likelihood is used as a performance measure used • to evaluate the features • The classifier used is a maximum likelihood with LDA and • method of testing was leave-one out

16. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Results and Discussions Table 2 (a): Confusion Matrix for Texture Features Table 2 (b): Confusion Matrix for Shape Features Table 1: Accuracies of individual features Table 3: Confusion Matrix for the optimum set of features after performing stepwise LDA

17. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Conclusion and Future Work • Accuracy of 78% is achieved with the combination of • texture, shape and age feature • Future work: • Better segmentation method • Implementations of rubber band straightening algorithm • Different algorithms for texture feature like gray-level run • length method, gray level difference method can be • implemented

18. ECE 8990: Automated Target Recognition Classification of Mammographic Masses References • “Normal mammogram classification based on regional analysis” -Yajie Sun; Babbs, C.F.; • Delp, E.J.; Circuits and Systems, 2002. MWSCAS- 2002. The 2002 45th Midwest • Symposium on, Volume: 2 , 4-7 Aug 2002 • http://marathon.csee.usf.edu/Mammography/Database.html • “Classification of Linear Structures in Mammographic Images - Reyer Zwiggelaar and • Caroline R.M. Boggis, Division of Computer Science, University of Portsmouth, Greater • Manchester Breast Screening Service, Withington Hospital, Manchester • “Gradient and texture analysis for the classification of Mammographic masses” Mudigonda, • N.R.; Rangayyan, R.; Desautels, J.E.L.; Medical Imaging, IEEE Transactions on, Volume: • 19, Issue: 10, Oct. 2000 Pages: 1032 – 1043 • http://marathon.csee.usf.edu/Mammography/software/heat heathusf_v1.1.0.html • “Texture Features for image Classification” Haralick , R.M; Shanugamk; Dinstein, I; • Systems,Man and Cybernetics,IEEE transactions on Vol.SMC- 3,No. 6 Nov. 1973 • Pages 610 – 621 • “Classifying Mammograhic Lesions Using Computerized Image Analysis” Kilday, J; Palmieri, F; • Fox, M.D; Medical Imaging, IEEE Transactions on, Volume: 12, No.4, 1993, Pages: 664 – 669 • “Classifying Mammographic Mass Shapes Using the wavelet transform Modulus-Maxima • Method” Bruce, L.M; Adhami, R.R; Medical Imaging, IEEE Transactions on, Volume: • 18,No.12,Dec 1999, Pages: 1170 – 1177 • “Discrimination of subtly different vegetative species via hyperspectral data” Mathur, A.; Bruce, • L.M.; Byrd, J; Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE • International Volume: 2 , 2002 Page(s): 805 –808 • “A Theoretical Comparison of Texture Algorithms ” Conners, R.WHarlow, C.A; Pattern Analysis • and Machine Intelligence, IEEE Transactions on, Vol: PAMI-2, No. 3, May 1980, Pages 204 - • 222

19. ECE 8990: Automated Target Recognition Classification of Mammographic Masses

20. ECE 8990: Automated Target Recognition Classification of Mammographic Masses Table 4: Confusion Matrix for all the features without age